Winter weather worsens gas-fired power outages in New York state
A multi-stage probabilistic framework to estimate gas-fired generator performance during extreme winter weather
Machine Learning
Summary
Winter storms often cause gas power plants to stop working well, but detailed data on outages is hard to get. To fix this, the authors created a three-step method using public weather and power use data to guess how likely a plant is to have trouble, how much power it can still produce, and how long the problem might last. They tested this method on New York from 2013 to 2022 and found that very cold weather and high electricity use make outages more likely, with outages sometimes lasting over half a day. Their approach can be used by utilities to improve planning and predict power problems better.
What this means in practice
- •For utility operators: Estimate gas generator outage risks and available capacity during winter to improve operational decisions and grid reliability.
- •For power system planners: Assess resource adequacy by integrating probabilistic winter performance estimates of gas generators into long-term planning models.
Authors
Sajjad Uddin Mahmud, Anamika Dubey
Abstract
Extreme winter weather has repeatedly disrupted gas-fired power generation in the United States, yet the plant-level data needed to systematically quantify outage risk remain proprietary. Using publicly available weather and electricity demand data together with anonymized generator contingency records from the North American Electric Reliability Corporation (NERC), we develop a three-stage Bayesian probabilistic framework for estimating winter-driven generator performance. Applied to New York State (2013--2022), the framework sequentially estimates: the hourly probability of a generator contingency event, the expected net available capacity conditioned on an event occurring, and the event duration. Colder conditions and higher electricity demand are associated with higher failure probability, lower retained capacity, and longer event duration. Under the most severe observed stress conditions, estimated mean hourly event probability reaches 24\% , while expected mean net available capacity falls to 13\% of nameplate rating. Full outage events have a median duration of 12.7 hours, while partial derating event duration increases from 2.4 to 7.1 hours with capacity loss severity. The proposed framework establishes a transferable baseline that utilities with access to plant-level records can directly extend to obtain more precise reliability estimates for operational planning and resource adequacy assessment.